Motivated by the desire to explore the process of combining inductive and deductive reasoning, we conducted a systematic literature review of articles that investigate the integration of machine learning and ontologies. The objective was to identify diverse techniques that incorporate both inductive reasoning (performed by machine learning) and deductive reasoning (performed by ontologies) into artificial intelligence systems. Our review, which included the analysis of 128 studies, allowed us to identify three main categories of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining, and learning and reasoning systems. We provide a comprehensive examination of all these categories, emphasizing the various machine learning algorithms utilized in the studies. Furthermore, we compared our classification with similar recent work in the field of hybrid AI and neuro-symbolic approaches.

翻译：受探索归纳推理与演绎推理相结合过程的驱动，我们对研究机器学习与本体整合的文献进行了系统性综述。目标在于识别将归纳推理（由机器学习执行）与演绎推理（由本体执行）共同融入人工智能系统的多元技术。我们的综述涵盖了对128项研究的分析，从中识别出机器学习与本体之间三种主要的混合模式：学习增强型本体、语义数据挖掘以及学习与推理系统。我们对所有类别进行了全面阐述，重点强调相关研究中采用的各类机器学习算法。此外，我们还将自身分类与近期混合人工智能及神经符号方法领域的类似工作进行了比较。